April 26, 2022
Data
No correlation? Inconclusive?
A different approach. 🤠 Data wrangling:
data <- read.csv("data/atd_weather_full.csv") %>%
rowwise() %>%
mutate(mean_atd = mean(c(atd_start, atd_mid, atd_end))) %>%
mutate(atd_rate = mean_atd / enrolled) %>%
mutate(course = factor(course)) %>%
mutate(weather = factor(weather)) %>%
mutate(date = as.Date(date, "%m/%d/%Y")) %>%
mutate(mean_temp = mean(temp_tm2, temp_tm1, temp_tm0, na.rm = TRUE))
First, Zoom attendance rate versus mean temp:
Zoom attendance rate versus the 7-day average of new COVID cases at the :
So, does this mean me might see relationships within individual classes?
…
Let’s find out!
3200, 3500, and 4400 look promising: we’ll make a plot matrix:
p3500 <- data_filtered %>%
filter(course == 3500) %>%
select(!course) %>%
ggpairs() +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.title = element_text(size = rel(0.85))) +
labs(title = "3500 Pairs")
(repeat as needed)